Overview

Dataset statistics

Number of variables14
Number of observations15000
Missing cells9643
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 MiB
Average record size in memory323.2 B

Variable types

Numeric9
DateTime1
Boolean1
Categorical2
Text1

Alerts

oxygen_saturation has 763 (5.1%) missing valuesMissing
heart_rate has 733 (4.9%) missing valuesMissing
temperature has 773 (5.2%) missing valuesMissing
systolic_bp has 721 (4.8%) missing valuesMissing
diastolic_bp has 738 (4.9%) missing valuesMissing
weight has 741 (4.9%) missing valuesMissing
blood_glucose has 752 (5.0%) missing valuesMissing
fatigue_level has 4422 (29.5%) missing valuesMissing
patient_id is uniformly distributedUniform
pain_level has 1387 (9.2%) zerosZeros

Reproduction

Analysis started2025-12-09 10:25:19.138109
Analysis finished2025-12-09 10:25:37.227704
Duration18.09 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

patient_id
Real number (ℝ)

Uniform 

Distinct500
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:25:37.387913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile475.05
Maximum500
Range499
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation144.34209
Coefficient of variation (CV)0.57621593
Kurtosis-1.2000096
Mean250.5
Median Absolute Deviation (MAD)125
Skewness0
Sum3757500
Variance20834.639
MonotonicityIncreasing
2025-12-09T10:25:37.598772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50030
 
0.2%
130
 
0.2%
230
 
0.2%
330
 
0.2%
430
 
0.2%
530
 
0.2%
630
 
0.2%
730
 
0.2%
830
 
0.2%
930
 
0.2%
Other values (490)14700
98.0%
ValueCountFrequency (%)
130
0.2%
230
0.2%
330
0.2%
430
0.2%
530
0.2%
630
0.2%
730
0.2%
830
0.2%
930
0.2%
1030
0.2%
ValueCountFrequency (%)
50030
0.2%
49930
0.2%
49830
0.2%
49730
0.2%
49630
0.2%
49530
0.2%
49430
0.2%
49330
0.2%
49230
0.2%
49130
0.2%
Distinct150
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
Minimum2024-03-01 06:00:00
Maximum2024-03-30 10:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-09T10:25:37.815671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:38.044489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

oxygen_saturation
Real number (ℝ)

Missing 

Distinct109
Distinct (%)0.8%
Missing763
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean97.0131
Minimum91.4
Maximum102.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:25:38.249632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum91.4
5-th percentile94.5
Q196
median97
Q398
95-th percentile99.5
Maximum102.3
Range10.9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5008326
Coefficient of variation (CV)0.015470411
Kurtosis-0.051288055
Mean97.0131
Median Absolute Deviation (MAD)1
Skewness-0.02483131
Sum1381175.5
Variance2.2524984
MonotonicityNot monotonic
2025-12-09T10:25:38.538308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.7403
 
2.7%
96.9386
 
2.6%
96.5384
 
2.6%
97.5382
 
2.5%
96.6382
 
2.5%
97373
 
2.5%
97.2370
 
2.5%
97.4367
 
2.4%
96.8365
 
2.4%
97.7361
 
2.4%
Other values (99)10464
69.8%
(Missing)763
 
5.1%
ValueCountFrequency (%)
91.41
< 0.1%
91.51
< 0.1%
91.61
< 0.1%
91.81
< 0.1%
91.91
< 0.1%
922
< 0.1%
92.11
< 0.1%
92.22
< 0.1%
92.32
< 0.1%
92.42
< 0.1%
ValueCountFrequency (%)
102.31
 
< 0.1%
102.21
 
< 0.1%
102.12
< 0.1%
1022
< 0.1%
101.92
< 0.1%
101.82
< 0.1%
101.71
 
< 0.1%
101.61
 
< 0.1%
101.54
< 0.1%
101.41
 
< 0.1%

heart_rate
Real number (ℝ)

Missing 

Distinct74
Distinct (%)0.5%
Missing733
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean74.4577
Minimum37
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:25:39.074799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile58
Q168
median74
Q381
95-th percentile91
Maximum113
Range76
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.9459257
Coefficient of variation (CV)0.1335782
Kurtosis-0.030765152
Mean74.4577
Median Absolute Deviation (MAD)7
Skewness0.023805072
Sum1062288
Variance98.921437
MonotonicityNot monotonic
2025-12-09T10:25:39.311877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73586
 
3.9%
76581
 
3.9%
74562
 
3.7%
71559
 
3.7%
75554
 
3.7%
78549
 
3.7%
72540
 
3.6%
69519
 
3.5%
79514
 
3.4%
80496
 
3.3%
Other values (64)8807
58.7%
(Missing)733
 
4.9%
ValueCountFrequency (%)
371
 
< 0.1%
401
 
< 0.1%
416
 
< 0.1%
421
 
< 0.1%
433
 
< 0.1%
444
 
< 0.1%
452
 
< 0.1%
469
0.1%
4713
0.1%
4819
0.1%
ValueCountFrequency (%)
1131
 
< 0.1%
1121
 
< 0.1%
1101
 
< 0.1%
1091
 
< 0.1%
1082
 
< 0.1%
1075
< 0.1%
1064
< 0.1%
1057
< 0.1%
1044
< 0.1%
1038
0.1%

temperature
Real number (ℝ)

Missing 

Distinct32
Distinct (%)0.2%
Missing773
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean36.800872
Minimum35
Maximum38.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:25:39.541705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile36.1
Q136.5
median36.8
Q337.1
95-th percentile37.5
Maximum38.3
Range3.3
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.40456842
Coefficient of variation (CV)0.010993447
Kurtosis0.025269245
Mean36.800872
Median Absolute Deviation (MAD)0.3
Skewness-0.006536166
Sum523566
Variance0.16367561
MonotonicityNot monotonic
2025-12-09T10:25:40.075023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
36.81450
9.7%
36.91361
9.1%
36.71361
9.1%
36.61271
8.5%
371237
 
8.2%
36.51037
 
6.9%
37.11034
 
6.9%
36.4854
 
5.7%
37.2820
 
5.5%
37.3696
 
4.6%
Other values (22)3106
20.7%
(Missing)773
 
5.2%
ValueCountFrequency (%)
351
 
< 0.1%
35.33
 
< 0.1%
35.43
 
< 0.1%
35.55
 
< 0.1%
35.628
 
0.2%
35.731
 
0.2%
35.859
 
0.4%
35.9116
0.8%
36216
1.4%
36.1286
1.9%
ValueCountFrequency (%)
38.32
 
< 0.1%
38.27
 
< 0.1%
38.15
 
< 0.1%
3810
 
0.1%
37.943
 
0.3%
37.864
 
0.4%
37.7110
 
0.7%
37.6219
1.5%
37.5315
2.1%
37.4462
3.1%

systolic_bp
Real number (ℝ)

Missing 

Distinct74
Distinct (%)0.5%
Missing721
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean119.4723
Minimum79
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:25:40.904863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile103
Q1113
median120
Q3126
95-th percentile136
Maximum159
Range80
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.9805273
Coefficient of variation (CV)0.08353842
Kurtosis-0.027694477
Mean119.4723
Median Absolute Deviation (MAD)7
Skewness-0.010595503
Sum1705945
Variance99.610926
MonotonicityNot monotonic
2025-12-09T10:25:41.849309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120616
 
4.1%
118577
 
3.8%
121577
 
3.8%
119557
 
3.7%
122543
 
3.6%
117542
 
3.6%
123520
 
3.5%
126508
 
3.4%
115503
 
3.4%
116493
 
3.3%
Other values (64)8843
59.0%
(Missing)721
 
4.8%
ValueCountFrequency (%)
791
 
< 0.1%
832
 
< 0.1%
841
 
< 0.1%
853
 
< 0.1%
862
 
< 0.1%
874
< 0.1%
884
< 0.1%
894
< 0.1%
905
< 0.1%
918
0.1%
ValueCountFrequency (%)
1591
 
< 0.1%
1562
 
< 0.1%
1551
 
< 0.1%
1523
 
< 0.1%
1511
 
< 0.1%
1503
 
< 0.1%
14911
0.1%
14817
0.1%
1479
0.1%
14618
0.1%

diastolic_bp
Real number (ℝ)

Missing 

Distinct53
Distinct (%)0.4%
Missing738
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean79.430865
Minimum51
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:25:42.543018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile68
Q175
median79
Q384
95-th percentile91
Maximum106
Range55
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.9814249
Coefficient of variation (CV)0.087893099
Kurtosis-0.00078949521
Mean79.430865
Median Absolute Deviation (MAD)5
Skewness-0.015270821
Sum1132843
Variance48.740294
MonotonicityNot monotonic
2025-12-09T10:25:42.770766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78814
 
5.4%
79812
 
5.4%
81797
 
5.3%
80792
 
5.3%
77768
 
5.1%
82740
 
4.9%
83729
 
4.9%
76721
 
4.8%
84657
 
4.4%
75655
 
4.4%
Other values (43)6777
45.2%
(Missing)738
 
4.9%
ValueCountFrequency (%)
511
 
< 0.1%
555
 
< 0.1%
563
 
< 0.1%
573
 
< 0.1%
588
 
0.1%
5910
 
0.1%
6019
 
0.1%
6122
 
0.1%
6239
0.3%
6355
0.4%
ValueCountFrequency (%)
1061
 
< 0.1%
1052
 
< 0.1%
1043
 
< 0.1%
1031
 
< 0.1%
1026
 
< 0.1%
1012
 
< 0.1%
1008
 
0.1%
9924
0.2%
9830
0.2%
9731
0.2%

weight
Real number (ℝ)

Missing 

Distinct862
Distinct (%)6.0%
Missing741
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean69.954779
Minimum12
Maximum122.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:25:43.762696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile45.5
Q159.9
median70
Q380
95-th percentile94.6
Maximum122.8
Range110.8
Interquartile range (IQR)20.1

Descriptive statistics

Standard deviation14.941255
Coefficient of variation (CV)0.21358448
Kurtosis0.0015045347
Mean69.954779
Median Absolute Deviation (MAD)10.1
Skewness-0.0084199199
Sum997485.2
Variance223.2411
MonotonicityNot monotonic
2025-12-09T10:25:43.950595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.254
 
0.4%
75.451
 
0.3%
74.150
 
0.3%
67.449
 
0.3%
6648
 
0.3%
71.348
 
0.3%
72.947
 
0.3%
77.447
 
0.3%
6746
 
0.3%
75.346
 
0.3%
Other values (852)13773
91.8%
(Missing)741
 
4.9%
ValueCountFrequency (%)
121
< 0.1%
12.41
< 0.1%
14.11
< 0.1%
15.72
< 0.1%
17.41
< 0.1%
18.11
< 0.1%
18.41
< 0.1%
18.71
< 0.1%
18.81
< 0.1%
19.31
< 0.1%
ValueCountFrequency (%)
122.81
< 0.1%
122.41
< 0.1%
1221
< 0.1%
1201
< 0.1%
119.71
< 0.1%
119.41
< 0.1%
118.51
< 0.1%
117.31
< 0.1%
116.81
< 0.1%
116.71
< 0.1%

blood_glucose
Real number (ℝ)

Missing 

Distinct137
Distinct (%)1.0%
Missing752
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean99.563237
Minimum12
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:25:44.123359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile67
Q186
median100
Q3113
95-th percentile132
Maximum173
Range161
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.865468
Coefficient of variation (CV)0.19952614
Kurtosis-0.014208108
Mean99.563237
Median Absolute Deviation (MAD)13
Skewness-0.054399417
Sum1418577
Variance394.63684
MonotonicityNot monotonic
2025-12-09T10:25:44.358498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103305
 
2.0%
102297
 
2.0%
95296
 
2.0%
101295
 
2.0%
100292
 
1.9%
104286
 
1.9%
106285
 
1.9%
99281
 
1.9%
97279
 
1.9%
94277
 
1.8%
Other values (127)11355
75.7%
(Missing)752
 
5.0%
ValueCountFrequency (%)
121
 
< 0.1%
262
 
< 0.1%
281
 
< 0.1%
302
 
< 0.1%
322
 
< 0.1%
333
< 0.1%
341
 
< 0.1%
351
 
< 0.1%
363
< 0.1%
376
< 0.1%
ValueCountFrequency (%)
1731
 
< 0.1%
1721
 
< 0.1%
1631
 
< 0.1%
1612
 
< 0.1%
1602
 
< 0.1%
1591
 
< 0.1%
1581
 
< 0.1%
1575
< 0.1%
1567
< 0.1%
1559
0.1%

pain_level
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9926
Minimum0
Maximum10
Zeros1387
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:25:44.517488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1707857
Coefficient of variation (CV)0.63509709
Kurtosis-1.2229191
Mean4.9926
Median Absolute Deviation (MAD)3
Skewness0.00335657
Sum74889
Variance10.053882
MonotonicityNot monotonic
2025-12-09T10:25:44.655873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
01387
9.2%
101383
9.2%
71380
9.2%
41379
9.2%
11377
9.2%
51371
9.1%
31364
9.1%
81357
9.0%
91349
9.0%
21339
8.9%
ValueCountFrequency (%)
01387
9.2%
11377
9.2%
21339
8.9%
31364
9.1%
41379
9.2%
51371
9.1%
61314
8.8%
71380
9.2%
81357
9.0%
91349
9.0%
ValueCountFrequency (%)
101383
9.2%
91349
9.0%
81357
9.0%
71380
9.2%
61314
8.8%
51371
9.1%
41379
9.2%
31364
9.1%
21339
8.9%
11377
9.2%

nausea
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
False
12019 
True
2981 
ValueCountFrequency (%)
False12019
80.1%
True2981
 
19.9%
2025-12-09T10:25:44.769666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

fatigue_level
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing4422
Missing (%)29.5%
Memory size804.1 KiB
Mild
6062 
Moderate
3000 
Severe
1516 

Length

Max length8
Median length4
Mean length5.4210626
Min length4

Characters and Unicode

Total characters57344
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSevere
2nd rowSevere
3rd rowMild
4th rowModerate
5th rowModerate

Common Values

ValueCountFrequency (%)
Mild6062
40.4%
Moderate3000
20.0%
Severe1516
 
10.1%
(Missing)4422
29.5%

Length

2025-12-09T10:25:44.934580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-09T10:25:45.043753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mild6062
57.3%
moderate3000
28.4%
severe1516
 
14.3%

Most occurring characters

ValueCountFrequency (%)
e10548
18.4%
d9062
15.8%
M9062
15.8%
l6062
10.6%
i6062
10.6%
r4516
7.9%
o3000
 
5.2%
a3000
 
5.2%
t3000
 
5.2%
S1516
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)57344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e10548
18.4%
d9062
15.8%
M9062
15.8%
l6062
10.6%
i6062
10.6%
r4516
7.9%
o3000
 
5.2%
a3000
 
5.2%
t3000
 
5.2%
S1516
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)57344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e10548
18.4%
d9062
15.8%
M9062
15.8%
l6062
10.6%
i6062
10.6%
r4516
7.9%
o3000
 
5.2%
a3000
 
5.2%
t3000
 
5.2%
S1516
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)57344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e10548
18.4%
d9062
15.8%
M9062
15.8%
l6062
10.6%
i6062
10.6%
r4516
7.9%
o3000
 
5.2%
a3000
 
5.2%
t3000
 
5.2%
S1516
 
2.6%

sleep_quality
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size790.7 KiB
Good
6133 
Fair
4434 
Excellent
2911 
Poor
1522 

Length

Max length9
Median length4
Mean length4.9703333
Min length4

Characters and Unicode

Total characters74555
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFair
2nd rowGood
3rd rowGood
4th rowExcellent
5th rowExcellent

Common Values

ValueCountFrequency (%)
Good6133
40.9%
Fair4434
29.6%
Excellent2911
19.4%
Poor1522
 
10.1%

Length

2025-12-09T10:25:45.184794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-09T10:25:45.341135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
good6133
40.9%
fair4434
29.6%
excellent2911
19.4%
poor1522
 
10.1%

Most occurring characters

ValueCountFrequency (%)
o15310
20.5%
G6133
8.2%
d6133
8.2%
r5956
 
8.0%
e5822
 
7.8%
l5822
 
7.8%
F4434
 
5.9%
i4434
 
5.9%
a4434
 
5.9%
x2911
 
3.9%
Other values (5)13166
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)74555
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o15310
20.5%
G6133
8.2%
d6133
8.2%
r5956
 
8.0%
e5822
 
7.8%
l5822
 
7.8%
F4434
 
5.9%
i4434
 
5.9%
a4434
 
5.9%
x2911
 
3.9%
Other values (5)13166
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)74555
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o15310
20.5%
G6133
8.2%
d6133
8.2%
r5956
 
8.0%
e5822
 
7.8%
l5822
 
7.8%
F4434
 
5.9%
i4434
 
5.9%
a4434
 
5.9%
x2911
 
3.9%
Other values (5)13166
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)74555
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o15310
20.5%
G6133
8.2%
d6133
8.2%
r5956
 
8.0%
e5822
 
7.8%
l5822
 
7.8%
F4434
 
5.9%
i4434
 
5.9%
a4434
 
5.9%
x2911
 
3.9%
Other values (5)13166
17.7%
Distinct61
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-12-09T10:25:45.708344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length57
Median length55
Mean length41.2404
Min length31

Characters and Unicode

Total characters618606
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPatient reports feeling severe with a pain level of 8.
2nd rowComplains of severe fatigue and no nausea.
3rd rowNoted elevated stress. Sleep was good.
4th rowNo major complaints. Sleep quality: Excellent.
5th rowNo major complaints. Sleep quality: Excellent.
ValueCountFrequency (%)
sleep6029
 
6.1%
of5985
 
6.0%
feeling5969
 
6.0%
patient5969
 
6.0%
no5430
 
5.5%
elevated3015
 
3.0%
noted3015
 
3.0%
stress3015
 
3.0%
was3015
 
3.0%
quality3014
 
3.0%
Other values (33)54506
55.1%
2025-12-09T10:25:46.166989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83962
13.6%
e78442
12.7%
a48011
 
7.8%
l46689
 
7.5%
t38329
 
6.2%
n36961
 
6.0%
o35486
 
5.7%
i34174
 
5.5%
s25283
 
4.1%
.21029
 
3.4%
Other values (33)170240
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)618606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
83962
13.6%
e78442
12.7%
a48011
 
7.8%
l46689
 
7.5%
t38329
 
6.2%
n36961
 
6.0%
o35486
 
5.7%
i34174
 
5.5%
s25283
 
4.1%
.21029
 
3.4%
Other values (33)170240
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)618606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
83962
13.6%
e78442
12.7%
a48011
 
7.8%
l46689
 
7.5%
t38329
 
6.2%
n36961
 
6.0%
o35486
 
5.7%
i34174
 
5.5%
s25283
 
4.1%
.21029
 
3.4%
Other values (33)170240
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)618606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
83962
13.6%
e78442
12.7%
a48011
 
7.8%
l46689
 
7.5%
t38329
 
6.2%
n36961
 
6.0%
o35486
 
5.7%
i34174
 
5.5%
s25283
 
4.1%
.21029
 
3.4%
Other values (33)170240
27.5%

Interactions

2025-12-09T10:25:35.054266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:20.701732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:22.207736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:24.044796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:26.217453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:29.142750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:30.848020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:32.215550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:33.565276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:35.187920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:20.853719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:22.366646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:24.250362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:26.410418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:29.391275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:30.980412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:32.363668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:33.764848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:35.336694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:21.016660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:22.507307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:24.441123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:26.569315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:29.642402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:31.130632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:32.500612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:33.912703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:35.493371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:21.197550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:22.663564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:24.868001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:26.730902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:29.829426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:31.291786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:32.667303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:34.065321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:35.660881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:21.351440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:22.812517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:25.264781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:26.880141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:29.997989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:31.447802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-09T10:25:35.835483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:21.542320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:22.968113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:25.585054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:28.107000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:30.199076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:31.617498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:32.975622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:34.398422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-09T10:25:25.780482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:28.338518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:30.382305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:31.765608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-09T10:25:25.919477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:28.640313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:30.555505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:31.914104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:33.261638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:34.708652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:36.259810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:22.055244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:23.759281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:26.070282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:28.866667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:30.708725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:32.070002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:33.419007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:25:34.898197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-09T10:25:46.322261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
blood_glucosediastolic_bpfatigue_levelheart_ratenauseaoxygen_saturationpain_levelpatient_idsleep_qualitysystolic_bptemperatureweight
blood_glucose1.000-0.0210.000-0.0080.0270.0020.0050.0100.000-0.006-0.018-0.007
diastolic_bp-0.0211.0000.0000.0040.015-0.002-0.0160.0130.0000.011-0.0060.000
fatigue_level0.0000.0001.0000.0000.0040.0000.0150.0000.0090.0000.0130.005
heart_rate-0.0080.0040.0001.0000.0180.009-0.007-0.0010.0040.006-0.0030.003
nausea0.0270.0150.0040.0181.0000.0150.0000.0150.0000.0240.0160.019
oxygen_saturation0.002-0.0020.0000.0090.0151.0000.010-0.0150.000-0.0070.0020.006
pain_level0.005-0.0160.015-0.0070.0000.0101.000-0.0010.000-0.0010.0010.023
patient_id0.0100.0130.000-0.0010.015-0.015-0.0011.0000.0000.010-0.0180.007
sleep_quality0.0000.0000.0090.0040.0000.0000.0000.0001.0000.0030.0000.000
systolic_bp-0.0060.0110.0000.0060.024-0.007-0.0010.0100.0031.0000.001-0.009
temperature-0.018-0.0060.013-0.0030.0160.0020.001-0.0180.0000.0011.0000.003
weight-0.0070.0000.0050.0030.0190.0060.0230.0070.000-0.0090.0031.000

Missing values

2025-12-09T10:25:36.474343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-09T10:25:36.692506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-09T10:25:37.059855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

patient_idtimestampoxygen_saturationheart_ratetemperaturesystolic_bpdiastolic_bpweightblood_glucosepain_levelnauseafatigue_levelsleep_qualityclinical_note
012024-03-01 08:00:0099.189.037.6109.093.046.8119.08NoSevereFairPatient reports feeling severe with a pain level of 8.
112024-03-02 08:00:0096.291.037.2122.080.084.4100.09NoSevereGoodComplains of severe fatigue and no nausea.
212024-03-03 08:00:00100.170.037.0131.072.080.4111.03YesMildGoodNoted elevated stress. Sleep was good.
312024-03-04 09:00:0097.354.037.0105.089.0NaN132.010NoModerateExcellentNo major complaints. Sleep quality: Excellent.
412024-03-05 09:00:0095.765.036.6112.085.062.0103.07NoModerateExcellentNo major complaints. Sleep quality: Excellent.
512024-03-06 06:00:0096.082.037.5110.078.065.668.00NoNaNExcellentPatient feeling generally well.
612024-03-07 08:00:0095.761.036.9127.068.066.8100.010NoSevereGoodPatient reports feeling severe with a pain level of 10.
712024-03-08 08:00:0097.778.0NaN122.073.068.6112.010NoNaNGoodNo major complaints. Sleep quality: Good.
812024-03-09 09:00:0096.080.036.7120.075.078.692.04NoSevereGoodComplains of severe fatigue and no nausea.
912024-03-10 07:00:0096.568.037.1126.0NaN74.3102.04NoMildExcellentComplains of mild fatigue and no nausea.
patient_idtimestampoxygen_saturationheart_ratetemperaturesystolic_bpdiastolic_bpweightblood_glucosepain_levelnauseafatigue_levelsleep_qualityclinical_note
149905002024-03-21 08:00:0096.573.036.9110.0NaNNaN78.00NoMildGoodComplains of mild fatigue and no nausea.
149915002024-03-22 09:00:0097.681.037.1124.089.059.983.00NoNaNGoodPatient reports feeling none with a pain level of 0.
149925002024-03-23 09:00:0097.770.036.5120.074.072.6122.01NoSevereGoodPatient feeling generally well.
149935002024-03-24 06:00:0096.382.036.8129.090.072.8103.06NoModerateExcellentPatient feeling generally well.
149945002024-03-25 08:00:0097.282.036.7146.079.072.4103.07NoMildFairComplains of mild fatigue and no nausea.
149955002024-03-26 07:00:0095.063.037.0113.0NaN37.2133.05YesMildGoodNo major complaints. Sleep quality: Good.
149965002024-03-27 10:00:0099.066.037.5125.071.070.080.00NoModerateExcellentComplains of moderate fatigue and no nausea.
149975002024-03-28 08:00:0096.261.037.6110.081.063.593.04NoNaNFairPatient reports feeling none with a pain level of 4.
149985002024-03-29 09:00:0096.6NaN36.4108.080.049.497.05NoNaNGoodNoted elevated stress. Sleep was good.
149995002024-03-30 08:00:0095.855.037.0126.066.056.693.00NoNaNPoorNoted elevated stress. Sleep was poor.